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INFERENCE ON THE DIET COMPOSITION OF PREDATORS USING FATTY ACID SIGNATURES: AN APPLICATION OF BAYESIAN INFERENCE ON LINEAR MIXING MODELS

dc.contributor.authorBlanchard, J. Wade
dc.contributor.copyright-releaseNot Applicableen_US
dc.contributor.degreeDoctor of Philosophyen_US
dc.contributor.departmentDepartment of Mathematics & Statistics - Statistics Divisionen_US
dc.contributor.ethics-approvalNot Applicableen_US
dc.contributor.external-examinerDr. Peter Guttorpen_US
dc.contributor.graduate-coordinatorDr. Ed Suskoen_US
dc.contributor.manuscriptsNot Applicableen_US
dc.contributor.thesis-readerDr. Sara Iversonen_US
dc.contributor.thesis-readerDr. David Hamiltonen_US
dc.contributor.thesis-readerDr. Michael Dowden_US
dc.contributor.thesis-supervisorDr. Bruce Smith, Dr. Don Bowenen_US
dc.date.accessioned2011-04-08T16:12:28Z
dc.date.available2011-04-08T16:12:28Z
dc.date.defence2011-04-04
dc.date.issued2011-04-08
dc.description.abstractDetermining the diet composition of predators is an important ingredient in many areas of ecology: understanding predator prey relationships, foraging behaviour of predators and consumption models to name a few. Iverson et al. (2004) developed a method based on the fatty acid signatures known as quantitative fatty acid signature analysis (QFASA). Fatty acids are the basic building blocks of most lipids and are indicative of diet, in the sense that higher level predators have limited ability to modify the fatty acids they ingest. Billheimer (2001) introduced a Bayesian compositional receptor model, where he apportioned the air pollution recorded a receptor site in Juneau Alaska into two components, woodstove smoke and automobile emissions. Building on this model we add components to allow for predator biosynthesis and differential fat content and also introduce a model which allows for design effects. Additionally we give some interesting results on the multi–modality of the logistic normal distribution. We also generalize the test of stationarity proposed by Priestley and Subba Rao (1969), based on evolutionary spectral ideas, as an alternative way of assessing when a MCMC sampler has reached its stationary distribution. xiven_US
dc.identifier.urihttp://hdl.handle.net/10222/13329
dc.language.isoenen_US
dc.titleINFERENCE ON THE DIET COMPOSITION OF PREDATORS USING FATTY ACID SIGNATURES: AN APPLICATION OF BAYESIAN INFERENCE ON LINEAR MIXING MODELSen_US

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